1.0 OVERVIEW
1.1 ABOUT AIRBNB
Airbnb is primarily an online accommodation rental marketplace which enables hosts to rent out their properties or rooms to guests. It has also expanded to offer “experiences” which the company describes as “unique activities we can do together, led by a world of hosts”. Since its inception in 2008, Airbnb has expanded into over 34,000 cities across 191 countries and opened its first international office in Hamburg, Germany in 2011. Though the company began as a privately owned business, it went public in 2020 at the beginning stages of COVID-19 pandemic.
Airbnb does not actually own the properties it offers for rent on its website, but rather generates its revenue through service fees to hosts and guests. In 2019, Airbnb was valued at 35 billion U.S. dollars. While SIngapore was their first choice to agrgresively expand in Asian markets, interestingly, Singapore is one of the global cities that has yet to legalise short-term rentals offered by Airbnb. Nonetheless, it will be intereesting to see how Airbnb is faring in Singapore, especially given the recent COVID-19.
1.2 PROBLEM STATEMENT
This exercise aims to investigate if the distribution of Airbnb listings in Singapore are affected by location factors and COVID-19. Therefore, my analysis is split into two parts aiming to answer the following two questions:
Section A: How is the distribution of Airbnb listings in Singapore affected by location factors (such as MRTs, tourist locations etc.)?
Section B: What is the impact of COVID-19 on Airbnb business in Singapore (comparing Airbnb listings data on June 2019 and June 2021)?
2.0 GETTING STARTED
This section covers installing the applicable R packages as well as importing the necessary data for analysis
2.1 SETTING UP THE ENVIRONMENT
The following R packages will be used in this analysis:
- sf: used for importing, managing and processing vector-based geospatial data in R
- spatstat: used for point pattern analysis to perform 1st and 2nd-order spatial point patterns analysis and derive kernel density estimation (KDE) layer
- raster: used for reading, writing, manipulating, analysing and modelling gridded spatial data (i.e. raster)
- maptools: used for manipulating geographic data, mainly to convert Spatial objects into ppp format of spatstat
- tmap: used for plotting cartographic quality static point patterns maps or interactive maps by using leaflet API
- tidyverse: consists of a family of R packages used for performing data science tasks such as importing, wrangling and visualising data.
- plotly: used to plot interactive plots
- ggthemes: is an extension of ggplot, with more advanced themes for plotting
packages <- c('maptools', 'sf', 'raster', 'spatstat', 'tmap', 'tidyverse', 'plotly', 'ggthemes')
for (p in packages){
if (!require(p, character.only = T)){
install.packages(p)
}
library(p, character.only = T)
}
2.2 IMPORTING DATA
The table below shows all the data that will be imported for this analysis
| Data Type | Name | Source |
|---|---|---|
| Geospatial | Master Plan 2014 Subzone Boundary (Web) | link |
| Geospatial | MRT & LRT Locations Aug 2021 | link |
| Geospatial | Singapore National Boundary | link |
| Aspatial | Singapore Airbnb Listings June 2019 | link |
| Aspatial | Singapore Airbnb Listings July 2021 | link |
| Aspatial | Tourism | OneMap |
| Aspatial | Hotels | OneMap |
DATA TALKING POINTS:
- In order to investigate if the distribution of Airbnb listings are affected by location factors, the following factors are considered in this analysis
- Tourism - Staying in near proximity to major tourist attractions would be convenient and hence would be an important consideration. A glance through this data set shows that it contains tourist attractions of various types such as heritage sites, museums, important buildings, parks etc
- Hotels - As hotels are often in the city center with proximity to important amenities such as convenient stores, money exchangers etc, it will be an important consideration as well to see if it impacts the distributions of Airbnb listing locations
- MRT & LRT Locations - Definitely MRT & LRT location is an important factor, especially in Singapore’s context where it is the most convenient mode of public transportation and often there is an accompanying bus stop, taxi stand and shopping center near majority of the MRT stations.
- In order to investigate the impact of COVID-19 on Airbnb listings, listings data from June 2019 and July 2021 are imported
2.2.1 IMPORTING GEOSPATIAL DATA
mpsz_sf <- st_read(dsn = "data/geospatial",
layer = "MP14_SUBZONE_WEB_PL")
Reading layer `MP14_SUBZONE_WEB_PL' from data source
`C:\Niharika-avula\IS415_blog\_posts\Take Home Exercise-2\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 323 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21MRT_LRT_Network_sf <- st_read(dsn = "data/geospatial",
layer = "MRTLRTStnPtt")
Reading layer `MRTLRTStnPtt' from data source
`C:\Niharika-avula\IS415_blog\_posts\Take Home Exercise-2\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 171 features and 3 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 6138.311 ymin: 27555.06 xmax: 45254.86 ymax: 47854.2
Projected CRS: SVY21sg_sf <- st_read(dsn = "data/geospatial", layer="CostalOutline")
Reading layer `CostalOutline' from data source
`C:\Niharika-avula\IS415_blog\_posts\Take Home Exercise-2\data\geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 60 features and 4 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 2663.926 ymin: 16357.98 xmax: 56047.79 ymax: 50244.03
Projected CRS: SVY212.2.2 IMPORTING ASPATIAL DATA
Listings_2019 <- read_csv("data/aspatial/listings_30062019.csv", show_col_types=FALSE)
Listings_2021 <- read_csv("data/aspatial/listings_19072021.csv", show_col_types=FALSE)
Hotels <- read_csv("data/aspatial/hotels.csv", show_col_types=FALSE)
Tourism <- read_csv("data/aspatial/tourism.csv", show_col_types=FALSE)
We have come to end of Section 2 with R packages installed and the Geospatial and Aspatial data imported, next section will cover data wrangling process of the imported data Verifying the project CRS of the above imported data
3.0 GEOSPATIAL DATA WRANGLING
This section covers all the steps taken in the pre-processing of the mpsz_sf and MRT_LRT_Network_sf data, which includes the following steps:
- Verifying and transforming the Coordinate system
- Handling missing values
- Handling invalid geometries
Took reference from senior’s project given as sample for this section
3.1 VERIFYING & TRANSFORMING CRS
MPSZ_SF
st_crs(mpsz_sf)
Coordinate Reference System:
User input: SVY21
wkt:
PROJCRS["SVY21",
BASEGEOGCRS["SVY21[WGS84]",
DATUM["World Geodetic System 1984",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]],
ID["EPSG",6326]],
PRIMEM["Greenwich",0,
ANGLEUNIT["Degree",0.0174532925199433]]],
CONVERSION["unnamed",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["Degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["Degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["(E)",east,
ORDER[1],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]],
AXIS["(N)",north,
ORDER[2],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]]]MRT_LRT_NETWORK_SF
st_crs(MRT_LRT_Network_sf)
Coordinate Reference System:
User input: SVY21
wkt:
PROJCRS["SVY21",
BASEGEOGCRS["SVY21[WGS84]",
DATUM["World Geodetic System 1984",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]],
ID["EPSG",6326]],
PRIMEM["Greenwich",0,
ANGLEUNIT["Degree",0.0174532925199433]]],
CONVERSION["unnamed",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["Degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["Degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["(E)",east,
ORDER[1],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]],
AXIS["(N)",north,
ORDER[2],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]]]SG_SF
st_crs(sg_sf)
Coordinate Reference System:
User input: SVY21
wkt:
PROJCRS["SVY21",
BASEGEOGCRS["SVY21[WGS84]",
DATUM["World Geodetic System 1984",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]],
ID["EPSG",6326]],
PRIMEM["Greenwich",0,
ANGLEUNIT["Degree",0.0174532925199433]]],
CONVERSION["unnamed",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["Degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["Degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["(E)",east,
ORDER[1],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]],
AXIS["(N)",north,
ORDER[2],
LENGTHUNIT["metre",1,
ID["EPSG",9001]]]]It can be seen that while the projected CRS is SVY21, the current EPSG Code is 9001, hence the next step is to assign the correct 3414 EPSG code
MPSZ_SF
mpsz_sf <- st_set_crs(mpsz_sf, 3414)
st_crs(mpsz_sf)
Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]MRT_LRT_NETWORK_SF
MRT_LRT_Network_sf <- st_set_crs(MRT_LRT_Network_sf, 3414)
st_crs(MRT_LRT_Network_sf)
Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]SG_SF
sg_sf <- st_set_crs(sg_sf, 3414)
st_crs(sg_sf)
Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]The correct projected CRS with EPSG Code 3413 is assigned and now we can move on to the next step
3.2 HANDLING MISSING VALUES
Checking for missing values as they can impact future calculations and visualisations.
MPSZ_SF
Simple feature collection with 0 features and 15 fields
Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] OBJECTID SUBZONE_NO SUBZONE_N SUBZONE_C CA_IND PLN_AREA_N
[7] PLN_AREA_C REGION_N REGION_C INC_CRC FMEL_UPD_D X_ADDR
[13] Y_ADDR SHAPE_Leng SHAPE_Area geometry
<0 rows> (or 0-length row.names)MRT_LRT_NETWORK_SF
Simple feature collection with 0 features and 3 fields
Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] OBJECTID STN_NAME STN_NO geometry
<0 rows> (or 0-length row.names)SG_SF
Simple feature collection with 0 features and 4 fields
Bounding box: xmin: NA ymin: NA xmax: NA ymax: NA
Projected CRS: SVY21 / Singapore TM
[1] GDO_GID MSLINK MAPID COSTAL_NAM geometry
<0 rows> (or 0-length row.names)There are no missing values in both mpsz_sf and MRT_LRT_Network_sf data
3.3 HANDLING INVALID GEOMETRIES
Checking for invalid geometries as they can impact future calculations and visualisations.
MPSZ_SF
MRT_LRT_NETWORK_SF
SG_SF
It can be seen that there are 9 invalid geometries in the mpsz_sf data and 1 invalid geometry in sg_sf data while there are no invalid geometries in MRT_LRT_Network_sf data, hence the invalid geometries need to be made valid
MPSZ_SF
SG_SF
Invalid geometries have been handled, there are no more invalid geometries in both datasets.
VISUALISING GEOSPATIAL DATA
Before we jump into the analysis, it is a good practice to visualise the geospatial data
MPSZ
plot(st_geometry(mpsz_sf))
SG_SF
plot(st_geometry(sg_sf))
MRT_LRT_NETWORK
tmap_mode("view")
tm_shape(mpsz_sf) +
tm_borders(alpha = 0.5) +
tmap_options(check.and.fix = TRUE) +
tm_shape(MRT_LRT_Network_sf) +
tm_dots(col = 'red', size = 0.02)
tmap_mode("plot")
With that, we have come to end of section 3 with the pre-processing of geospatial mpsz_sf and MRT_LRT_Network_sf data completed, next section will be looking into the same process for Aspatial data.
4.0 ASPATIAL DATA WRANGLING
This section covers all the steps taken in the pre-processing of the Listings_2019, Listings_2021, Hotels and Tourism data, which includes the following steps:
- Handling missing values
- Converting into sf objects and transforming the Coordinate system
Let’s have a glimpse at the data first
glimpse(Listings_2019)
Rows: 8,293
Columns: 16
$ id <dbl> 49091, 50646, 56334, 71609, 7~
$ name <chr> "COZICOMFORT LONG TERM STAY R~
$ host_id <dbl> 266763, 227796, 266763, 36704~
$ host_name <chr> "Francesca", "Sujatha", "Fran~
$ neighbourhood_group <chr> "North Region", "Central Regi~
$ neighbourhood <chr> "Woodlands", "Bukit Timah", "~
$ latitude <dbl> 1.44255, 1.33235, 1.44246, 1.~
$ longitude <dbl> 103.7958, 103.7852, 103.7967,~
$ room_type <chr> "Private room", "Private room~
$ price <dbl> 81, 80, 68, 200, 92, 102, 203~
$ minimum_nights <dbl> 180, 90, 6, 1, 1, 1, 1, 7, 30~
$ number_of_reviews <dbl> 1, 18, 20, 12, 20, 35, 23, 15~
$ last_review <date> 2013-10-21, 2014-12-26, 2015~
$ reviews_per_month <dbl> 0.01, 0.28, 0.21, 0.13, 0.21,~
$ calculated_host_listings_count <dbl> 2, 1, 2, 9, 9, 9, 9, 1, 4, 4,~
$ availability_365 <dbl> 365, 365, 365, 353, 353, 348,~Listings_2019 has latitude and longitude features
glimpse(Listings_2021)
Rows: 4,252
Columns: 18
$ id <dbl> 50646, 71609, 71896, 71903, 2~
$ name <chr> "Pleasant Room along Bukit Ti~
$ host_id <dbl> 227796, 367042, 367042, 36704~
$ host_name <chr> "Sujatha", "Belinda", "Belind~
$ neighbourhood_group <chr> "Central Region", "East Regio~
$ neighbourhood <chr> "Bukit Timah", "Tampines", "T~
$ latitude <dbl> 1.33432, 1.34537, 1.34754, 1.~
$ longitude <dbl> 103.7852, 103.9589, 103.9596,~
$ room_type <chr> "Private room", "Private room~
$ price <dbl> 80, 178, 81, 81, 52, 40, 72, ~
$ minimum_nights <dbl> 90, 90, 90, 90, 14, 14, 90, 8~
$ number_of_reviews <dbl> 18, 20, 24, 48, 20, 13, 133, ~
$ last_review <date> 2014-07-08, 2019-12-28, 2014~
$ reviews_per_month <dbl> 0.22, 0.28, 0.33, 0.67, 0.20,~
$ calculated_host_listings_count <dbl> 1, 4, 4, 4, 50, 50, 7, 1, 50,~
$ availability_365 <dbl> 365, 365, 365, 365, 353, 364,~
$ number_of_reviews_ltm <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,~
$ license <chr> NA, NA, NA, NA, NA, NA, NA, N~Listings_2021 has latitude and longitude features
glimpse(Hotels)
Rows: 422
Columns: 9
$ NAME <chr> "Jayleen Clarke Quay Hotel", "JEN Singapor~
$ ADDRESSPOSTALCODE <dbl> 59390, 238858, 249716, 399041, 238485, 399~
$ ADDRESSSTREETNAME <chr> "25 New Bridge Road", "277 Orchard Road , ~
$ HYPERLINK <chr> "jayleenclarkequay@gmail.com", "singaporeo~
$ TOTALROOMS <dbl> 20, 499, 565, 42, 81, 33, 17, 634, 56, 451~
$ KEEPERNAME <chr> "James Federick Chong Kah Yean", "Kuok Oon~
$ Lat <dbl> 1.288715, 1.300510, 1.304271, 1.311586, 1.~
$ Lng <dbl> 103.8475, 103.8392, 103.8239, 103.8775, 10~
$ ICON_NAME <chr> "hotel.gif", "hotel.gif", "hotel.gif", "ho~Hotels has Lat and Lng features
glimpse(Tourism)
Rows: 107
Columns: 17
$ NAME <chr> "Chinatown Heritage Centre, Singapore", "T~
$ DESCRIPTION <chr> "Experience how Singapore’s early Chinese ~
$ ADDRESSSTREETNAME <chr> "48 Pagoda Street", "158 Telok Ayer Street~
$ HYPERLINK <chr> "http://www.singaporechinatown.com.sg/", "~
$ PHOTOURL <chr> "www.yoursingapore.com/content/dam/desktop~
$ URL_PATH <chr> "www.yoursingapore.com/en/see-do-singapore~
$ IMAGE_ALT_TEXT <chr> "Learn more about local Chinese culture at~
$ PHOTOCREDITS <chr> "Joel Chua DY", "Joel Chua DY", "Joel Chua~
$ LASTMODIFIED <dttm> 2015-11-02 02:16:52, 2015-11-02 02:20:57,~
$ LATITUDE <dbl> 1.283510, 1.280940, 1.310070, 1.277219, 1.~
$ LONGTITUDE <dbl> 103.8444, 103.8476, 103.8994, 103.8373, 10~
$ META_DESCRIPTION <chr> "At the Chinatown Heritage Centre, experie~
$ OPENING_HOURS <chr> "Daily, 9am – 8pm,Last entry at 7pm.*China~
$ Lat <dbl> 1.283510, 1.280940, 1.310070, 1.277219, 1.~
$ Lng <dbl> 103.8444, 103.8476, 103.8994, 103.8373, 10~
$ ICON_NAME <chr> "tourist_spot.gif", "tourist_spot.gif", "t~
$ ADDRESSPOSTALCODE <dbl> NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA,~Tourism has both Lat and Lng and latitude and longitude features, this needs to be further explored to check if there are missing values
4.1 HANDLING MISSING VALUES
LISTINGS_2019 DATA
No Missing values in the latitude and longitude columns in Listing_2019 data
LISTINGS_2021 DATA
No Missing values in the latitude and longitude columns in Listing_2021 data
HOTELS DATA
No Missing values in the Lng and Lat columns in Hotels data
TOURISM DATA
No Missing values in the Lng and Lat columns in Tourism data
It can be seen that there is one missing values in the Longitude and Latitude columns in Tourism data, hence will be using the Lat and Lng for Tourism data.
In order to identify the tourist attraction with the missing values
Tourism[(is.na(Tourism$LONGTITUDE)),]
# A tibble: 1 x 17
NAME DESCRIPTION ADDRESSSTREETNAME HYPERLINK PHOTOURL URL_PATH
<chr> <chr> <chr> <chr> <chr> <chr>
1 Cruis~ Watch the Si~ <NA> <NA> www.your~ www.your~
# ... with 11 more variables: IMAGE_ALT_TEXT <chr>,
# PHOTOCREDITS <chr>, LASTMODIFIED <dttm>, LATITUDE <dbl>,
# LONGTITUDE <dbl>, META_DESCRIPTION <chr>, OPENING_HOURS <chr>,
# Lat <dbl>, Lng <dbl>, ICON_NAME <chr>, ADDRESSPOSTALCODE <dbl>Tourism[(is.na(Tourism$LATITUDE)),]
# A tibble: 1 x 17
NAME DESCRIPTION ADDRESSSTREETNAME HYPERLINK PHOTOURL URL_PATH
<chr> <chr> <chr> <chr> <chr> <chr>
1 Cruis~ Watch the Si~ <NA> <NA> www.your~ www.your~
# ... with 11 more variables: IMAGE_ALT_TEXT <chr>,
# PHOTOCREDITS <chr>, LASTMODIFIED <dttm>, LATITUDE <dbl>,
# LONGTITUDE <dbl>, META_DESCRIPTION <chr>, OPENING_HOURS <chr>,
# Lat <dbl>, Lng <dbl>, ICON_NAME <chr>, ADDRESSPOSTALCODE <dbl>The tourist attraction with missing value happens to be cruise. As we are looking into Airbnb listings, I suppose it will not matter a lot whether they are close to cruise or not, hence going to remove cruise form the dataset.
glimpse(Tourism)
Rows: 106
Columns: 17
$ NAME <chr> "Chinatown Heritage Centre, Singapore", "T~
$ DESCRIPTION <chr> "Experience how Singapore’s early Chinese ~
$ ADDRESSSTREETNAME <chr> "48 Pagoda Street", "158 Telok Ayer Street~
$ HYPERLINK <chr> "http://www.singaporechinatown.com.sg/", "~
$ PHOTOURL <chr> "www.yoursingapore.com/content/dam/desktop~
$ URL_PATH <chr> "www.yoursingapore.com/en/see-do-singapore~
$ IMAGE_ALT_TEXT <chr> "Learn more about local Chinese culture at~
$ PHOTOCREDITS <chr> "Joel Chua DY", "Joel Chua DY", "Joel Chua~
$ LASTMODIFIED <dttm> 2015-11-02 02:16:52, 2015-11-02 02:20:57,~
$ LATITUDE <dbl> 1.283510, 1.280940, 1.310070, 1.277219, 1.~
$ LONGTITUDE <dbl> 103.8444, 103.8476, 103.8994, 103.8373, 10~
$ META_DESCRIPTION <chr> "At the Chinatown Heritage Centre, experie~
$ OPENING_HOURS <chr> "Daily, 9am – 8pm,Last entry at 7pm.*China~
$ Lat <dbl> 1.283510, 1.280940, 1.310070, 1.277219, 1.~
$ Lng <dbl> 103.8444, 103.8476, 103.8994, 103.8373, 10~
$ ICON_NAME <chr> "tourist_spot.gif", "tourist_spot.gif", "t~
$ ADDRESSPOSTALCODE <dbl> NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA,~Therefore, the number rows decreased from 107 to 106 in the Tourism dataset
4.2 CONVERTING INTO SF OBJECTS & TRANSFORMING CRS
This converts the below dataframes into sf objects and transform the projected CRS to 3414
Listings_2019_sf <- st_as_sf(Listings_2019,
coords = c("longitude",
"latitude"),
crs = 4326) %>%
st_transform(crs=3414)
Listings_2021_sf <- st_as_sf(Listings_2021,
coords = c("longitude",
"latitude"),
crs = 4326) %>%
st_transform(crs=3414)
Hotels_sf <- st_as_sf(Hotels,
coords = c("Lng",
"Lat"),
crs = 4326) %>%
st_transform(crs=3414)
Tourism_sf <- st_as_sf(Tourism,
coords = c("Lng",
"Lat"),
crs = 4326) %>%
st_transform(crs=3414)
Checking if the correct CRS and EPSG code is assigned
LISTINGS_2019_SF
st_crs(Listings_2019_sf)
Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]LISTINGS_2021_SF
st_crs(Listings_2021_sf)
Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]st_crs(Hotels_sf)
Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]TOURISM_SF
st_crs(Tourism_sf)
Coordinate Reference System:
User input: EPSG:3414
wkt:
PROJCRS["SVY21 / Singapore TM",
BASEGEOGCRS["SVY21",
DATUM["SVY21",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4757]],
CONVERSION["Singapore Transverse Mercator",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",1.36666666666667,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",103.833333333333,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",1,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",28001.642,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",38744.572,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["northing (N)",north,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["easting (E)",east,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre, engineering survey, topographic mapping."],
AREA["Singapore - onshore and offshore."],
BBOX[1.13,103.59,1.47,104.07]],
ID["EPSG",3414]]We have come to end of section 4 with the pre-processing of aspatial Listings_2019, Listings_2021, Hotels and Tourism data completed
5.0 COMBINING GEOSPATIAL & ASPATIAL DATA
This section covers the steps taken to convert both the geospatial and aspatial data into the appropriate formats for performing spatial point pattern analysis.
Geospatial: mpsz_sf, sg_sf, and MRT_LRT_Network_sf
Aspatial: Listings_2019_sf, Listings_2021_sf, Hotels_sf and Tourism_sf
5.1 CONVERTING SF DATA FRAME TO SP’S SPATIAL* CLASS
With the below code chunk, all of the above geospatial and aspatial data frames are converted into sp’s Spatial* class
mpsz <- as_Spatial(mpsz_sf)
MRT_LRT_Network <-as_Spatial(MRT_LRT_Network_sf)
sg <- as_Spatial(sg_sf)
Listings_2019 <- as_Spatial(Listings_2019_sf)
Listings_2021 <- as_Spatial(Listings_2021_sf)
Hotels <- as_Spatial(Hotels_sf)
Tourism <- as_Spatial(Tourism_sf)
Checking if the conversion is successful
MPSZ
mpsz
class : SpatialPolygonsDataFrame
features : 323
extent : 2667.538, 56396.44, 15748.72, 50256.33 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
variables : 15
names : OBJECTID, SUBZONE_NO, SUBZONE_N, SUBZONE_C, CA_IND, PLN_AREA_N, PLN_AREA_C, REGION_N, REGION_C, INC_CRC, FMEL_UPD_D, X_ADDR, Y_ADDR, SHAPE_Leng, SHAPE_Area
min values : 1, 1, ADMIRALTY, AMSZ01, N, ANG MO KIO, AM, CENTRAL REGION, CR, 00F5E30B5C9B7AD8, 16409, 5092.8949, 19579.069, 871.554887798, 39437.9352703
max values : 323, 17, YUNNAN, YSSZ09, Y, YISHUN, YS, WEST REGION, WR, FFCCF172717C2EAF, 16409, 50424.7923, 49552.7904, 68083.9364708, 69748298.792 MRT_LRT_NETWORK
MRT_LRT_Network
class : SpatialPointsDataFrame
features : 171
extent : 6138.311, 45254.86, 27555.06, 47854.2 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
variables : 3
names : OBJECTID, STN_NAME, STN_NO
min values : 1, ADMIRALTY MRT STATION, BP1
max values : 199, YISHUN MRT STATION, TE8 SG
sg
class : SpatialPolygonsDataFrame
features : 60
extent : 2663.926, 56047.79, 16357.98, 50244.03 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
variables : 4
names : GDO_GID, MSLINK, MAPID, COSTAL_NAM
min values : 1, 1, 0, ISLAND LINK
max values : 60, 67, 0, SINGAPORE - MAIN ISLAND LISTINGS_2019
Listings_2019
class : SpatialPointsDataFrame
features : 8293
extent : 7215.566, 44098.31, 25166.35, 49226.35 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
variables : 14
names : id, name, host_id, host_name, neighbourhood_group, neighbourhood, room_type, price, minimum_nights, number_of_reviews, last_review, reviews_per_month, calculated_host_listings_count, availability_365
min values : 49091, -, 23666, (Email hidden by Airbnb), Central Region, Ang Mo Kio, Entire home/apt, 0, 1, 0, 15656, 0.01, 1, 0
max values : 36053005, ZR2- NEW! Sunny & Modern Apt 4 mins to Orchard Rd, 271165196, Zuzana, West Region, Yishun, Shared room, 13999, 1000, 308, 18072, 12.09, 277, 365 LISTINGS_2021
Listings_2021
class : SpatialPointsDataFrame
features : 4252
extent : 7406.989, 43337.89, 25330, 48391.55 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
variables : 16
names : id, name, host_id, host_name, neighbourhood_group, neighbourhood, room_type, price, minimum_nights, number_of_reviews, last_review, reviews_per_month, calculated_host_listings_count, availability_365, number_of_reviews_ltm, ...
min values : 50646, !! CozyRoom@City Center,Little India,FarrerParkMRT, 23666, <U+4F73><U+4F73>, Central Region, Ang Mo Kio, Entire home/apt, 13, 1, 0, 15456, 0.01, 1, 0, 0, ...
max values : 51122424, Zimmer nah der MRT (Downtown Line), 411376818, Zuzu, West Region, Yishun, Shared room, 13400, 1000, 370, 18826, 77, 165, 365, 195, ... HOTELS
Hotels
class : SpatialPointsDataFrame
features : 422
extent : 5939.241, 45334.18, 25379.44, 44562.4 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
variables : 7
names : NAME, ADDRESSPOSTALCODE, ADDRESSSTREETNAME, HYPERLINK, TOTALROOMS, KEEPERNAME, ICON_NAME
min values : 30 BENCOOLEN, 18956, 1 Bayfront Avenue, 96ytlim@gmail.com, 4, Adel Aramouni, hotel.gif
max values : YotelAir Singapore Changi Airport, 819666, 99 IRRAWADDY ROAD, # 22-00 ROYAL SQUARE AT NOVENA, zubair@dam.com.sg, 2561, Zhang YuanQing, hotel.gif TOURISM
Tourism
class : SpatialPointsDataFrame
features : 106
extent : 11380.23, 43659.54, 22869.34, 47596.73 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs
variables : 15
names : NAME, DESCRIPTION, ADDRESSSTREETNAME, HYPERLINK, PHOTOURL, URL_PATH, IMAGE_ALT_TEXT, PHOTOCREDITS, LASTMODIFIED, LATITUDE, LONGTITUDE, META_DESCRIPTION, OPENING_HOURS, ICON_NAME, ADDRESSPOSTALCODE
min values : Adventure Cove Waterpark™ Singapore, A feat of engineering, an architectural statement and a sheer aesthetic triumph, Marina Bay Sands<sup>®</sup> has upped the ante for buildings in Singapore., 1 Beach Road, http://acm.org.sg/, /content/dam/desktop/global/see-do-singapore/architecture/hajjah-fatimah-mosque-carousel01-rect.jpg, www.yoursingapore.com/en/see-do-singapore/architecture/historical/capitol-building-singapore.html, Adults and kids of all ages who are not even science buffs will have fun at the Singapore Science Centre., ©Darren Soh/National Gallery, 1427691447.648, 1.2230965, 103.68398, A tranquil patch of imperial China in the west of Singapore is pleasant respite from the bustle of the city., 50th storey Skybridge, Daily, 9am –10pm, tourist_spot.gif, 0
max values : Victoria Theatre Singapore, With so many attractions packed into this 15-km stretch of beaches, you’ll never run out of things to do., Seng Poh Road and Tiong Bahru Road, https://www.pub.gov.sg/marinabarrage, www.yoursingapore.com/content/dam/desktop/global/see-do-singapore/recreation-leisure/universal-studios-singapore-carousel01-rect.jpg, www.yoursingapore.com/en/see-do-singapore/recreation-leisure/viewpoints/singapore-flyer.html, Whether you prefer water sports, rollerblading or cycling, find a myriad of things to do at East Coast Park, Singapore., Wildlife Reserves Singapore, 1446544541.364, 1.44672, 103.97403, With the Henderson Waves bridge, form meets function to stunning effect., Visits are by appointment only.Visitors must sign up in advance for heritage tours which fall on:Monday, 2pm – 3pm,Tuesday, 6.30pm – 7.30pm,Thursday, 10am – 11am,Saturday, 11am – 12pm, tourist_spot.gif, 0 All of the above data have been converted into their respective sp’s Spatial * classes
5.2 CONVERTING THE SPATIAL* CLASS INTO GENERIC SP FORMAT
This section covers the steps taken to convert Spatial* classes into Spatial objects
mpsz_sp <- as(mpsz, "SpatialPolygons")
sg_sp <- as(sg, "SpatialPolygons")
MRT_LRT_Network_sp <-as(MRT_LRT_Network, "SpatialPoints")
Listings_2019_sp <- as(Listings_2019, "SpatialPoints")
Listings_2021_sp <- as(Listings_2021, "SpatialPoints")
Hotels_sp <- as(Hotels, "SpatialPoints")
Tourism_sp <- as(Tourism, "SpatialPoints")
Checking if the conversion is successful
MPSZ_SP
mpsz_sp
class : SpatialPolygons
features : 323
extent : 2667.538, 56396.44, 15748.72, 50256.33 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs SG_SP
sg_sp
class : SpatialPolygons
features : 60
extent : 2663.926, 56047.79, 16357.98, 50244.03 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs MRT_LRT_NETWORK_SP
MRT_LRT_Network_sp
class : SpatialPoints
features : 171
extent : 6138.311, 45254.86, 27555.06, 47854.2 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs LISTINGS_2019_SP
Listings_2019_sp
class : SpatialPoints
features : 8293
extent : 7215.566, 44098.31, 25166.35, 49226.35 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs LISTINGS_2021_SP
Listings_2021_sp
class : SpatialPoints
features : 4252
extent : 7406.989, 43337.89, 25330, 48391.55 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs HOTELS_SP
Hotels_sp
class : SpatialPoints
features : 422
extent : 5939.241, 45334.18, 25379.44, 44562.4 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs TOURISM_SP
Tourism_sp
class : SpatialPoints
features : 106
extent : 11380.23, 43659.54, 22869.34, 47596.73 (xmin, xmax, ymin, ymax)
crs : +proj=tmerc +lat_0=1.36666666666667 +lon_0=103.833333333333 +k=1 +x_0=28001.642 +y_0=38744.572 +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs All of the above data have been converted successfully
5.3 CONVERTING THE GENERIC SP FORMAT INTO SPATSTAT’S PPP FORMAT
This section covers the steps taken to convert Spatial data into spatista’s ppp objects
MRT_LRT_Network_ppp <-as(MRT_LRT_Network_sp, "ppp")
Listings_2019_ppp <- as(Listings_2019_sp, "ppp")
Listings_2021_ppp <- as(Listings_2021_sp, "ppp")
Hotels_ppp <- as(Hotels_sp, "ppp")
Tourism_ppp <- as(Tourism_sp, "ppp")
Checking the summary statistics of the newly created ppp objects
MRT_LRT_NETWORK_PPP
summary(MRT_LRT_Network_ppp)
Planar point pattern: 171 points
Average intensity 2.153565e-07 points per square unit
Coordinates are given to 3 decimal places
i.e. rounded to the nearest multiple of 0.001 units
Window: rectangle = [6138.31, 45254.86] x [27555.06, 47854.2] units
(39120 x 20300 units)
Window area = 794032000 square unitsLISTINGS_2019_PPP
summary(Listings_2019_ppp)
Planar point pattern: 8293 points
Average intensity 9.345289e-06 points per square unit
*Pattern contains duplicated points*
Coordinates are given to 3 decimal places
i.e. rounded to the nearest multiple of 0.001 units
Window: rectangle = [7215.57, 44098.31] x [25166.35, 49226.35] units
(36880 x 24060 units)
Window area = 887399000 square unitsLISTINGS_2021_PPP
summary(Listings_2021_ppp)
Planar point pattern: 4252 points
Average intensity 5.131408e-06 points per square unit
*Pattern contains duplicated points*
Coordinates are given to 3 decimal places
i.e. rounded to the nearest multiple of 0.001 units
Window: rectangle = [7406.99, 43337.89] x [25330, 48391.55] units
(35930 x 23060 units)
Window area = 828622000 square unitsHOTELS_PPP
summary(Hotels_ppp)
Planar point pattern: 422 points
Average intensity 5.58414e-07 points per square unit
*Pattern contains duplicated points*
Coordinates are given to 3 decimal places
i.e. rounded to the nearest multiple of 0.001 units
Window: rectangle = [5939.24, 45334.18] x [25379.44, 44562.4] units
(39390 x 19180 units)
Window area = 755712000 square unitsTOURISM_PPP
summary(Tourism_ppp)
Planar point pattern: 106 points
Average intensity 1.328016e-07 points per square unit
*Pattern contains duplicated points*
Coordinates are given to 3 decimal places
i.e. rounded to the nearest multiple of 0.001 units
Window: rectangle = [11380.23, 43659.54] x [22869.34, 47596.73] units
(32280 x 24730 units)
Window area = 798183000 square unitsThe summary of the above data shows that the following ppp pbjects have duplicated values which willlbe handled in the next section.
LISTINGS_2019_PPP, LISTINGS_2021_PPP, HOTELS_PPP and TOURISM_PPP
5.3 REMOVING DUPLICATE POINTS USING JITTERING FUNCTION
This section covers the steps taken to handle the duplicated values in the below ppp objects. The best solution to avoid deleting useful information is jittering, which will add a small perturbation to the duplicate points so that they do not occupy the exact same space
Listings_2019_ppp_jit <- rjitter(Listings_2019_ppp,
retry=TRUE,
nsim=1,
drop=TRUE)
Listings_2021_ppp_jit <- rjitter(Listings_2021_ppp,
retry=TRUE,
nsim=1,
drop=TRUE)
Hotels_ppp_jit <- rjitter(Hotels_ppp,
retry=TRUE,
nsim=1,
drop=TRUE)
Tourism_ppp_jit <- rjitter(Tourism_ppp,
retry=TRUE,
nsim=1,
drop=TRUE)
Checking if there are any duplicated values
LISTINGS_2019_PPP_JIT
any(duplicated(Listings_2019_ppp_jit))
[1] FALSELISTINGS_2021_PPP_JIT
any(duplicated(Listings_2021_ppp_jit))
[1] FALSEHOTELS_PPP_JIT
any(duplicated(Hotels_ppp_jit))
[1] FALSETOURISM_PPP_JIT
any(duplicated(Tourism_ppp_jit))
[1] FALSEThere are no duplicated values and hence we can move on to the next crucial step which is creating the owin object
5.4 CREATING OWIN OBJECT
When analysing spatial point patterns, it is a good practice to confine the analysis with a geographical area like Singapore boundary. In spatstat, an object called owin is specially designed to represent this polygonal region.
sg_owin <- as(sg_sp, "owin")
plot(sg_owin)
summary(sg_owin)
Window: polygonal boundary
61 separate polygons (1 hole)
vertices area relative.area
polygon 1 38 1.56140e+04 2.09e-05
polygon 2 735 4.69093e+06 6.27e-03
polygon 3 49 1.66986e+04 2.23e-05
polygon 4 76 3.12332e+05 4.17e-04
polygon 5 5141 6.36179e+08 8.50e-01
polygon 6 42 5.58317e+04 7.46e-05
polygon 7 67 1.31354e+06 1.75e-03
polygon 8 15 4.46420e+03 5.96e-06
polygon 9 14 5.46674e+03 7.30e-06
polygon 10 37 5.26194e+03 7.03e-06
polygon 11 53 3.44003e+04 4.59e-05
polygon 12 74 5.82234e+04 7.78e-05
polygon 13 69 5.63134e+04 7.52e-05
polygon 14 143 1.45139e+05 1.94e-04
polygon 15 165 3.38736e+05 4.52e-04
polygon 16 130 9.40465e+04 1.26e-04
polygon 17 19 1.80977e+03 2.42e-06
polygon 18 16 2.01046e+03 2.69e-06
polygon 19 93 4.30642e+05 5.75e-04
polygon 20 90 4.15092e+05 5.54e-04
polygon 21 721 1.92795e+06 2.57e-03
polygon 22 330 1.11896e+06 1.49e-03
polygon 23 115 9.28394e+05 1.24e-03
polygon 24 37 1.01705e+04 1.36e-05
polygon 25 25 1.66227e+04 2.22e-05
polygon 26 10 2.14507e+03 2.86e-06
polygon 27 190 2.02489e+05 2.70e-04
polygon 28 175 9.25904e+05 1.24e-03
polygon 29 1990 9.99217e+06 1.33e-02
polygon 30 (hole) 3 -1.06765e+00 -1.43e-09
polygon 31 38 2.42492e+04 3.24e-05
polygon 32 24 6.35239e+03 8.48e-06
polygon 33 53 6.35791e+05 8.49e-04
polygon 34 41 1.60161e+04 2.14e-05
polygon 35 22 2.54368e+03 3.40e-06
polygon 36 30 1.08382e+04 1.45e-05
polygon 37 327 2.16921e+06 2.90e-03
polygon 38 111 6.62927e+05 8.85e-04
polygon 39 90 1.15991e+05 1.55e-04
polygon 40 98 6.26829e+04 8.37e-05
polygon 41 415 3.25384e+06 4.35e-03
polygon 42 222 1.51142e+06 2.02e-03
polygon 43 107 6.33039e+05 8.45e-04
polygon 44 7 2.48299e+03 3.32e-06
polygon 45 17 3.28303e+04 4.38e-05
polygon 46 26 8.34758e+03 1.11e-05
polygon 47 177 4.67446e+05 6.24e-04
polygon 48 16 3.19460e+03 4.27e-06
polygon 49 15 4.87296e+03 6.51e-06
polygon 50 66 1.61841e+04 2.16e-05
polygon 51 149 5.63430e+06 7.53e-03
polygon 52 609 2.62570e+07 3.51e-02
polygon 53 8 7.82256e+03 1.04e-05
polygon 54 976 2.33447e+07 3.12e-02
polygon 55 55 8.25379e+04 1.10e-04
polygon 56 976 2.33447e+07 3.12e-02
polygon 57 61 3.33449e+05 4.45e-04
polygon 58 6 1.68410e+04 2.25e-05
polygon 59 4 9.45963e+03 1.26e-05
polygon 60 46 6.99702e+05 9.35e-04
polygon 61 13 7.00873e+04 9.36e-05
enclosing rectangle: [2663.93, 56047.79] x [16357.98, 50244.03] units
(53380 x 33890 units)
Window area = 748741000 square units
Fraction of frame area: 0.4145.5 COMBINING POINT EVENTS OBJECT AND OWIN OBJECT
This is the last step of this entire data wrangling process of combining geospatial and aspatial data, which is to extract the relevant events that are located within Singapore.
MRT_LRT_network_ppp_sg = MRT_LRT_Network_ppp[sg_owin]
Listings_2019_ppp_sg = Listings_2019_ppp_jit[sg_owin]
Listings_2021_ppp_sg = Listings_2021_ppp_jit[sg_owin]
Hotels_ppp_sg = Hotels_ppp_jit[sg_owin]
Tourism_ppp_sg = Tourism_ppp_jit[sg_owin]
The output object above combined both the point and polygon feature in one ppp object class and it is good practice to visualise the combined data.
plot(MRT_LRT_network_ppp_sg)
plot(Hotels_ppp_sg)
plot(Tourism_ppp_sg)
Now we are all set for the performing the actual analysis!
6.0 [SECTION A] AIRBNB DISTRIBUTION ANALYSIS IN 2019
This section A covers the following two sets of analysis to determine how the distribution of Airbnb listings in Singapore is affected by location factors of MRTs, tourist attractions and hotels
- Exploratory Spatial Data Analysis
- Second-order Spatial Point Patterns Analysis
6.1 [SECTION A] EXPLORATORY SPATIAL DATA ANALYSIS: KERNEL DENSITY MAPS
This section covers the steps taken to derive kernel density maps of Airbnb listings, hotels, MRT services, and tourist attractions.
6.1.1 RESCALLING KDE VALUES
The first step is to rescale and convert the unit of measurement from meter to kilometer, this step will ensure a better visualisation for comprehension
MRT_LRT_network_ppp_sg_km <- rescale(MRT_LRT_network_ppp_sg, 1000, 'km')
Listings_2019_ppp_sg_km <- rescale(Listings_2019_ppp_sg, 1000, 'km')
Listings_2021_ppp_sg_km <- rescale(Listings_2021_ppp_sg, 1000, 'km')
Hotels_ppp_sg_km <- rescale(Hotels_ppp_sg, 1000, 'km')
Tourism_ppp_sg_km <- rescale(Tourism_ppp_sg, 1000, 'km')
6.1.2 COMPUTING KERNEL DENSITY ESTIMATION
This section covers the steps taken to compute the Kernel Density estimation, there are many factors to consider and that will be detailed below with the reason
- BANDWIDTH METHODS
- bw.diggle() is the automatic bandwidth selection method however there are also other recommended methods such as bw.CvL(), bw.scott() or bw.ppl()
- bw.ppl() is used when the pattern consists predominantly of tight clusters, while bw.diggle() is used to detect a single tight cluster in the midst of random noise
- Therefore, most factors (hotels, tourist attractions, listings) appear to be clustered in the central region as expected, so bw.diggle() will be a suitable for our use
- KERNEL METHODS
- Default smoothing kernel used is gaussian while Other smoothing methods include epanechnikov, quartic and disc, so will be using the default
- FIXED AND ADAPTIVE KDE BANDWIDTH
- Fixed bandwidth method is very sensitive to highly skew distribution of spatial point patterns
- Adaptive bandwidth method is used to overcome the this problem of skewness in data
- Therefore, as most of our data appears to be clustered in the central region, will use fixed bandwidth method
kde_MRT_LRT_network_sg_bw <- density(MRT_LRT_network_ppp_sg_km,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
kde_listings_2019_sg_bw <- density(Listings_2019_ppp_sg_km,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
kde_hotels_sg_bw <- density(Hotels_ppp_sg_km,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
kde_tourism_sg_bw <- density(Tourism_ppp_sg_km,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
6.1.3 PLOTTING KERNEL DENSITY MAPS
AIRBNB LISTINGS IN JUNE 2019
plot(kde_listings_2019_sg_bw)
It can be seen that most of the listings are clustered in the central region closer to the financial and business district where we have majority of our main attractions.
SINGAPORE MRT NETWORK
plot(kde_MRT_LRT_network_sg_bw)
It can be seen that the MRT stations are also mostly clustered around the central region where we have the business district, this is where we have many interchanges as well such as Dhoby Gaut, City Hall etc. There is another cluster in the north-east region which is probably because of the Punggol and Seng Kang LRT stations and the same explanation could also be applied to the cluster in the western part of Singapore where the bukit panjang LRT line is located.
HOTELS AROUND SNGAPORE
plot(kde_hotels_sg_bw)
As expected, the hotels are also clustered in the central region as per convenience of all travellers (business, tourist etc). This looks very similar to KDE map of Airbnb Listings, which makes me wonder if majority of the Airbnb listings are not only by local residents offering their houses but also smaller boutique hotels using Airbnb as a platform to reach out to the potential cutomers.
SINGAPORE TOURIST ATTRACTIONS
plot(kde_tourism_sg_bw)
I guess it does make sense that most of our tourist attractions like heritage sites, shopping districts and other attractions are all clustered in the central region
6.1.4 CONVERTING KDE OUTPUT INTO GRID OBJECT
gridded_kde_MRT_LRT_network_sg_bw <- as.SpatialGridDataFrame.im(kde_MRT_LRT_network_sg_bw)
gridded_kde_listings_2019_sg_bw <- as.SpatialGridDataFrame.im(kde_listings_2019_sg_bw)
gridded_kde_hotels_sg_bw <- as.SpatialGridDataFrame.im(kde_hotels_sg_bw)
gridded_kde_tourism_sg_bw <- as.SpatialGridDataFrame.im(kde_tourism_sg_bw)
Plotting the results:
AIRBNB LISTINGS IN 2019spplot(gridded_kde_listings_2019_sg_bw)
SINGAPORE MRT NETWORK
spplot(gridded_kde_MRT_LRT_network_sg_bw)
HOTELS AROUND SNGAPORE
plot(gridded_kde_hotels_sg_bw)
SINGAPORE TOURIST ATTRACTIONS
plot(gridded_kde_tourism_sg_bw)
6.1.5 CONVERTING GRIDDED OUTPUT INTO RASTER
kde_MRT_LRT_network_sg_bw_raster <- raster(gridded_kde_MRT_LRT_network_sg_bw)
projection(kde_MRT_LRT_network_sg_bw_raster) <- CRS("+init=EPSG:3414")
kde_listings_2019_sg_bw_raster <- raster(gridded_kde_listings_2019_sg_bw)
projection(kde_listings_2019_sg_bw_raster) <- CRS("+init=EPSG:3414")
kde_hotels_sg_bw_raster <- raster(gridded_kde_hotels_sg_bw)
projection(kde_hotels_sg_bw_raster) <- CRS("+init=EPSG:3414")
kde_tourism_sg_bw <- raster(gridded_kde_tourism_sg_bw)
projection(kde_tourism_sg_bw) <- CRS("+init=EPSG:3414")
6.1.5 DISPLAYING KERNEL DENSITY MAPS ON OPENSTREETMAP
tmap_mode("view")
tm_shape(kde_listings_2019_sg_bw_raster) +
tm_raster("v", title = "Airbnb listings June 2019", alpha = 0.7, palette = "Reds") +
tm_layout(legend.position = c("right", "bottom"), frame = FALSE) +
tm_shape(kde_MRT_LRT_network_sg_bw_raster) +
tm_raster("v", title = "MRT Network", alpha = 0.7, palette = "Blues") +
tm_layout(legend.position = c("right", "bottom"), frame = FALSE) +
tm_shape(kde_hotels_sg_bw_raster) +
tm_raster("v", title = "Hotels", alpha = 0.7, palette = "Greens") +
tm_layout(legend.position = c("right", "bottom"), frame = FALSE) +
tm_shape(kde_tourism_sg_bw) +
tm_raster("v", title = "Tourist Attractions", alpha = 0.7, palette = "Purples") +
tm_layout(legend.position = c("right", "bottom"), frame = FALSE)
tmap_mode("plot")
ADVANTAGES OF KERNEL DENSITY OVER POINT MAP
- Kernel Density maps are more effective in visualizing clusters as compared to point maps because intensity is calculated and displayed in Kernel density map.While, we can also see clusters in point maps, it is not very clear especially when htere are many clusters close to each other
OBSERVATIONS
It can be seen from the above Kernel Density maps that Airbnb listings, Tourist Attractions as well as Hotels seem to be clustered mostly around the central region, it might be more worth while to focus only on the central region
6.1.6 CREATING CENTRAL REGION OWIN OBJECT
In the following code chunk, we will be creating the Central region Owin from the mpsz_sf data object
Central_region_sf = mpsz_sf[mpsz_sf$REGION_N == "CENTRAL REGION",]
Central_region <- as_Spatial(Central_region_sf)
Central_region_SP = as(Central_region, "SpatialPolygons")
Central_region_owin <- as(Central_region_SP, "owin")
plot(Central_region_owin)
6.1.7 COMBINING POINT EVENTS OBJECT AND CENTRAL REGION OWIN OBJECT
MRT_LRT_network_ppp_CR = MRT_LRT_Network_ppp[Central_region_owin]
Listings_2019_ppp_CR = Listings_2019_ppp_jit[Central_region_owin]
Listings_2021_ppp_CR = Listings_2021_ppp_jit[Central_region_owin]
Hotels_ppp_CR = Hotels_ppp_jit[Central_region_owin]
Tourism_ppp_CR = Tourism_ppp_jit[Central_region_owin]
The output object above combined both the point and polygon feature in one ppp object class and it is good practice to visualise the combined data.
plot(MRT_LRT_network_ppp_CR)
plot(Hotels_ppp_CR)
plot(Tourism_ppp_CR)
6.1.8 RESCALLING KDE VALUES FOR CENTRAL REGION
MRT_LRT_network_ppp_CR_km <- rescale(MRT_LRT_network_ppp_CR, 1000, 'km')
Listings_2019_ppp_CR_km <- rescale(Listings_2019_ppp_CR, 1000, 'km')
Listings_2021_ppp_CR_km <- rescale(Listings_2021_ppp_CR, 1000, 'km')
Hotels_ppp_CR_km <- rescale(Hotels_ppp_CR, 1000, 'km')
Tourism_ppp_CR_km <- rescale(Tourism_ppp_CR, 1000, 'km')
6.1.9 COMPUTING KERNEL DENSITY ESTIMATION FOR CENTRAL REGION
kde_MRT_LRT_network_CR_bw <- density(MRT_LRT_network_ppp_CR_km,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
kde_listings_2019_CR_bw <- density(Listings_2019_ppp_CR_km,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
kde_hotels_CR_bw <- density(Hotels_ppp_CR_km,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
kde_tourism_CR_bw <- density(Tourism_ppp_CR_km,
sigma=bw.diggle,
edge=TRUE,
kernel="gaussian")
PLOTTING KERNEL DENSITY MAPS FOR CENTRAL REGION:
AIRBNB LISTINGS IN JUNE 2019
plot(kde_listings_2019_CR_bw)
SINGAPORE MRT NETWORK
plot(kde_MRT_LRT_network_CR_bw)
HOTELS AROUND SNGAPORE
plot(kde_hotels_CR_bw)
SINGAPORE TOURIST ATTRACTIONS
plot(kde_tourism_CR_bw)
Based on the above central region Kernel density maps, it can be seen that indeed Airbnb listings, MRT stations, hotels and tourist attractions are all mostly located within Central region of Singapore.
6.2 [SECTION A] SECOND-ORDER SPATIAL POINT PATTERN ANALYSIS
This section covers the steps taken to perform Second-order Spatial Point Patterns Analysis to determine if there is any impact of chosen factors such as Hotel distribution, MRT network and Tourist attraction on Airbnb listings. In the earlier section, we have identified that Airbnb Listings are mostly clustered in the central region and therefore the focus of this part of the analysis will also be on central region.
For the purpose of this analysis, will be applying the Second-order Multi-type Point Patterns Analysis using Cross L-Function
6.2.1 SECOND-ORDER SPATIAL POINT PATTERN ANALYSIS [Airbnb listings & Hotels]
CREATING NEW COMBINED PPP OBJECT
This new combined ppp object is created from Airbnblistings and Hotels ppp objects
Combined_A_H_ppp <- superimpose(Airbnblistings2019 = Listings_2019_ppp_CR,
Hotelsdist = Hotels_ppp_CR)
MULTI-TYPE POINT PATTERNS ANALYSIS
Combined_A_H_Lcross <- Lcross(Combined_A_H_ppp, i="Airbnblistings2019", j="Hotelsdist", correction='border')
plot(Combined_A_H_Lcross, . -r ~ r,
xlab = "distance(m)",
xlim=c(0, 500))
PERFORMING CSR ON CROSS L-FUNCTION
The hypothesis and test are as follows:
Ho = The distribution of Airbnb listings 2019 and Hotels in the central region are spatially independent.
H1= The distribution of Airbnb listings 2019 and Hotels in the central region are NOT at spatially independent.
The null hypothesis will be rejected if p-value is smaller than alpha value of 0.001 (i.e. at 99.9% confident interval).
In order to perform the CSR test, the envelope() of spatstat package will be used
Combined_A_H_Lcross.csr <- envelope(Combined_A_H_ppp, Lcross, i="Airbnblistings2019", j="Hotelsdist", correction='border', nsim=999)
Generating 999 simulations of CSR ...
1, 2, 3, ......10 [etd 27:22] .........20 [etd 28:03] .........
30 [etd 30:09] .........40 [etd 29:23] .........50 [etd 29:00] ........
.60 [etd 29:07] .........70 [etd 28:50] .........80 [etd 29:03] .......
..90 [etd 28:28] .........100 [etd 27:59] .........110 [etd 27:34] ......
...120 [etd 26:58] .........130 [etd 26:27] .........140 [etd 26:03] .....
....150 [etd 25:34] .........160 [etd 25:08] .........170 [etd 24:43] ....
.....180 [etd 24:20] .........190 [etd 23:54] .........200 [etd 23:30] ...
......210 [etd 23:09] .........220 [etd 22:46] .........230 [etd 22:23] ..
.......240 [etd 22:01] .........250 [etd 21:41] .........260 [etd 21:23] .
........270 [etd 21:04] .........280 [etd 20:44] .........290
[etd 20:29] .........300 [etd 20:14] .........310 [etd 19:57] .........
320 [etd 19:37] .........330 [etd 19:20] .........340 [etd 19:02] ........
.350 [etd 18:46] .........360 [etd 18:27] .........370 [etd 18:09] .......
..380 [etd 17:49] .........390 [etd 17:31] .........400 [etd 17:14] ......
...410 [etd 16:56] .........420 [etd 16:38] .........430 [etd 16:19] .....
....440 [etd 16:00] .........450 [etd 15:45] .........460 [etd 15:27] ....
.....470 [etd 15:09] .........480 [etd 14:50] .........490 [etd 14:33] ...
......500 [etd 14:15] .........510 [etd 13:57] .........520 [etd 13:40] ..
.......530 [etd 13:22] .........540 [etd 13:04] .........550 [etd 12:47] .
........560 [etd 12:29] .........570 [etd 12:12] .........580
[etd 11:55] .........590 [etd 11:37] .........600 [etd 11:22] .........
610 [etd 11:05] .........620 [etd 10:47] .........630 [etd 10:30] ........
.640 [etd 10:12] .........650 [etd 9:56] .........660 [etd 9:39] .......
..670 [etd 9:22] .........680 [etd 9:04] .........690 [etd 8:50] ......
...700 [etd 8:34] .........710 [etd 8:16] .........720 [etd 7:59] .....
....730 [etd 7:41] .........740 [etd 7:24] .........750 [etd 7:07] ....
.....760 [etd 6:50] .........770 [etd 6:32] .........780 [etd 6:15] ...
......790 [etd 5:57] .........800 [etd 5:40] .........810 [etd 5:23] ..
.......820 [etd 5:06] .........830 [etd 4:49] .........840 [etd 4:31] .
........850 [etd 4:14] .........860 [etd 3:57] .........870
[etd 3:40] .........880 [etd 3:23] .........890 [etd 3:06] .........
900 [etd 2:49] .........910 [etd 2:32] .........920 [etd 2:15] ........
.930 [etd 1:57] .........940 [etd 1:40] .........950 [etd 1:23] .......
..960 [etd 1:06] .........970 [etd 49 sec] .........980 [etd 32 sec] ......
...990 [etd 15 sec] ........ 999.
Done.
OBERVATIONS: * The black line is above the red line and beyond the upper confidence envelop, hence the null hypothesis is rejected at 99.9% confidence interval. * The distribution of Airbnb listings 2019 and Hotels in the central region are not spatially independent.
6.2.2 SECOND-ORDER SPATIAL POINT PATTERN ANALYSIS [Airbnb listings & Tourist Attractions]
CREATING NEW COMBINED PPP OBJECT
This new combined ppp object is created from Airbnblistings and Tourism ppp objects
Combined_A_T_ppp <- superimpose(Airbnblistings2019 = Listings_2019_ppp_CR,
TouristAttractions = Tourism_ppp_CR)
MULTI-TYPE POINT PATTERNS ANALYSIS
Combined_A_T_Lcross <- Lcross(Combined_A_T_ppp, i="Airbnblistings2019", j="TouristAttractions", correction='border')
plot(Combined_A_T_Lcross, . -r ~ r,
xlab = "distance(m)",
xlim=c(0, 500))
PERFORMING CSR ON CROSS L-FUNCTION
The hypothesis and test are as follows:
Ho = The distribution of Airbnb listings 2019 and Tourist Attractions in the central region are spatially independent.
H1= The distribution of Airbnb listings 2019 and Tourist Attractions in the central region are NOT at spatially independent.
The null hypothesis will be rejected if p-value is smaller than alpha value of 0.001 (i.e. at 99.9% confident interval).
In order to perform the CSR test, the envelope() of spatstat package will be used
Combined_A_T_Lcross.csr <- envelope(Combined_A_T_ppp, Lcross, i="Airbnblistings2019", j="TouristAttractions", correction='border', nsim=999)
Generating 999 simulations of CSR ...
1, 2, 3, ......10 [etd 27:32] .........20 [etd 27:25] .........
30 [etd 27:39] .........40 [etd 27:15] .........50 [etd 26:32] ........
.60 [etd 26:21] .........70 [etd 25:47] .........80 [etd 25:25] .......
..90 [etd 24:53] .........100 [etd 24:24] .........110 [etd 24:14] ......
...120 [etd 23:59] .........130 [etd 23:37] .........140 [etd 23:19] .....
....150 [etd 23:02] .........160 [etd 22:46] .........170 [etd 22:22] ....
.....180 [etd 22:16] .........190 [etd 21:57] .........200 [etd 21:42] ...
......210 [etd 21:24] .........220 [etd 21:12] .........230 [etd 20:55] ..
.......240 [etd 20:39] .........250 [etd 20:24] .........260 [etd 20:10] .
........270 [etd 19:52] .........280 [etd 19:35] .........290
[etd 19:15] .........300 [etd 18:57] .........310 [etd 18:38] .........
320 [etd 18:21] .........330 [etd 18:02] .........340 [etd 17:46] ........
.350 [etd 17:27] .........360 [etd 17:09] .........370 [etd 16:53] .......
..380 [etd 16:34] .........390 [etd 16:16] .........400 [etd 15:58] ......
...410 [etd 15:42] .........420 [etd 15:26] .........430 [etd 15:13] .....
....440 [etd 15:00] .........450 [etd 14:51] .........460 [etd 14:36] ....
.....470 [etd 14:20] .........480 [etd 14:05] .........490 [etd 13:49] ...
......500 [etd 13:34] .........510 [etd 13:19] .........520 [etd 13:04] ..
.......530 [etd 12:48] .........540 [etd 12:32] .........550 [etd 12:16] .
........560 [etd 12:00] .........570 [etd 11:43] .........580
[etd 11:26] .........590 [etd 11:09] .........600 [etd 10:52] .........
610 [etd 10:35] .........620 [etd 10:18] .........630 [etd 10:01] ........
.640 [etd 9:44] .........650 [etd 9:27] .........660 [etd 9:10] .......
..670 [etd 8:53] .........680 [etd 8:37] .........690 [etd 8:20] ......
...700 [etd 8:04] .........710 [etd 7:47] .........720 [etd 7:31] .....
....730 [etd 7:14] .........740 [etd 6:58] .........750 [etd 6:42] ....
.....760 [etd 6:25] .........770 [etd 6:09] .........780 [etd 5:53] ...
......790 [etd 5:38] .........800 [etd 5:27] .........810 [etd 5:15] ..
.......820 [etd 5:03] .........830 [etd 4:49] .........840 [etd 4:36] .
........850 [etd 4:21] .........860 [etd 4:05] .........870
[etd 3:50] .........880 [etd 3:34] .........890 [etd 3:18] .........
900 [etd 3:02] .........910 [etd 2:43] .........920 [etd 2:25] ........
.930 [etd 2:06] .........940 [etd 1:48] .........950 [etd 1:29] .......
..960 [etd 1:11] .........970 [etd 53 sec] .........980 [etd 34 sec] ......
...990 [etd 16 sec] ........ 999.
Done.
OBERVATIONS:
The black line is above the red line and beyond the upper confidence envelop for distance less than approximately 150m, hence the null hypothesis is rejected at 99.9% confidence interval.The distribution of Airbnb listings 2019 and tourist attractions in the central region are not spatially independent for less than 150m
The black line is below the red line and beyond the lower confidence envelop for distance more than approximately 150m, hence the null hypothesis is rejected at 99.9% confidence interval.The distribution of Airbnb listings 2019 and tourist attractions in the central region are not spatially independent for more than 150m
6.2.3 SECOND-ORDER SPATIAL POINT PATTERN ANALYSIS [Airbnb listings & MRT NETWORK]
CREATING NEW COMBINED PPP OBJECT
This new combined ppp object is created from Airbnblistings and Tourism ppp objects
Combined_A_M_ppp <- superimpose(Airbnblistings2019 = Listings_2019_ppp_CR,
MRTNetwork = MRT_LRT_network_ppp_CR)
MULTI-TYPE POINT PATTERNS ANALYSIS
Combined_A_M_Lcross <- Lcross(Combined_A_M_ppp, i="Airbnblistings2019", j="MRTNetwork", correction='border')
plot(Combined_A_M_Lcross, . -r ~ r,
xlab = "distance(m)",
xlim=c(0, 500))
PERFORMING CSR ON CROSS L-FUNCTION
The hypothesis and test are as follows:
Ho = The distribution of Airbnb listings 2019 and MRT Network in the central region are spatially independent.
H1= The distribution of Airbnb listings 2019 and MRT Network in the central region are NOT at spatially independent.
The null hypothesis will be rejected if p-value is smaller than alpha value of 0.001 (i.e. at 99.9% confident interval).
In order to perform the CSR test, the envelope() of spatstat package will be used
Combined_A_M_Lcross.csr <- envelope(Combined_A_M_ppp, Lcross, i="Airbnblistings2019", j="MRTNetwork", correction='border', nsim=999)
Generating 999 simulations of CSR ...
1, 2, 3, ......10 [etd 25:31] .........20 [etd 25:23] .........
30 [etd 24:53] .........40 [etd 24:50] .........50 [etd 24:27] ........
.60 [etd 24:43] .........70 [etd 24:21] .........80 [etd 23:58] .......
..90 [etd 23:42] .........100 [etd 23:19] .........110 [etd 23:13] ......
...120 [etd 22:54] .........130 [etd 22:35] .........140 [etd 22:15] .....
....150 [etd 22:01] .........160 [etd 21:43] .........170 [etd 21:25] ....
.....180 [etd 21:07] .........190 [etd 20:51] .........200 [etd 20:35] ...
......210 [etd 20:18] .........220 [etd 20:01] .........230 [etd 19:45] ..
.......240 [etd 19:31] .........250 [etd 19:14] .........260 [etd 18:59] .
........270 [etd 18:51] .........280 [etd 18:37] .........290
[etd 18:20] .........300 [etd 18:05] .........310 [etd 18:24] .........
320 [etd 18:05] .........330 [etd 17:45] .........340 [etd 17:29] ........
.350 [etd 17:12] .........360 [etd 16:54] .........370 [etd 16:39] .......
..380 [etd 16:21] .........390 [etd 16:04] .........400 [etd 15:48] ......
...410 [etd 15:30] .........420 [etd 15:13] .........430 [etd 14:58] .....
....440 [etd 14:42] .........450 [etd 14:25] .........460 [etd 14:08] ....
.....470 [etd 13:52] .........480 [etd 13:37] .........490 [etd 13:20] ...
......500 [etd 13:04] .........510 [etd 12:48] .........520 [etd 12:32] ..
.......530 [etd 12:16] .........540 [etd 11:59] .........550 [etd 11:43] .
........560 [etd 11:42] .........570 [etd 11:37] .........580
[etd 11:19] .........590 [etd 11:01] .........600 [etd 10:45] .........
610 [etd 10:28] .........620 [etd 10:11] .........630 [etd 9:55] ........
.640 [etd 9:38] .........650 [etd 9:22] .........660 [etd 9:06] .......
..670 [etd 8:49] .........680 [etd 8:33] .........690 [etd 8:16] ......
...700 [etd 8:00] .........710 [etd 7:43] .........720 [etd 7:27] .....
....730 [etd 7:11] .........740 [etd 6:55] .........750 [etd 6:38] ....
.....760 [etd 6:22] .........770 [etd 6:12] .........780 [etd 6:02] ...
......790 [etd 5:51] .........800 [etd 5:39] .........810 [etd 5:27] ..
.......820 [etd 5:14] .........830 [etd 5:01] .........840 [etd 4:47] .
........850 [etd 4:32] .........860 [etd 4:16] .........870
[etd 4:00] .........880 [etd 3:41] .........890 [etd 3:22] .........
900 [etd 3:03] .........910 [etd 2:44] .........920 [etd 2:25] ........
.930 [etd 2:07] .........940 [etd 1:48] .........950 [etd 1:30] .......
..960 [etd 1:11] .........970 [etd 53 sec] .........980 [etd 35 sec] ......
...990 [etd 16 sec] ........ 999.
Done.
OBERVATIONS:
The black line is above the red line and within the upper confidence envelop for distance less than approximately 150m and greater than 420m, hence the null hypothesis cannot be rejected at 99.9% confidence interval.The distribution of Airbnb listings 2019 and MRT network in the central region are spatially independent for less than 150m
The black line is below the red line and within the lower confidence envelop for distance more than approximately 150m and less than approximately 420m, hence the null hypothesis cannot be rejected at 99.9% confidence interval.The distribution of Airbnb listings 2019 and MRT network in the central region are spatially independent for less than 150m
7.0 [Section B] IMPACT OF COVID-19
This section B covers the following two sets of analysis to determine What is the impact of COVID-19 on Airbnb business in Singapore (comparing Airbnb listings data on June 2019 and June 2021)
- Exploratory Spatial Data Analysis
- Second-order Spatial Point Patterns Analysis
7.1 [SECTION B] EXPLORATORY SPATIAL DATA ANALYSIS: KERNEL DENSITY MAPS]
This section covers the steps taken to derive kernel density maps of Airbnb listings by room type as of June 2019 and June 2021
It is always best practice to visualize your data first to increase our understanding
AIRBNB LISTINGS JUNE 2019
tmap_mode("view")
tm_shape(Listings_2019_sf) +
tm_dots(col = 'room_type', size = 0.02, title = "Room Type")
AIRBNB LISTINGS JUNE 2021
tmap_mode("view")
tm_shape(Listings_2021_sf) +
tm_dots(col = 'room_type', size = 0.02, title = "Room Type")
tmap_mode("plot")
From the above two plots, we can see that while there are three room types in the June 2019 data, there are four room types in June 2021 data, therefore in order to make a fair comparison, Hotel room type will be excluded from June 2021 data.
7.2 [SECTION B] SECOND-ORDER SPATIAL POINT PATTERN ANALYSIS